# A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization

arXiv: https://arxiv.org/abs/1405.7229 · HTML: https://mlsanity.com/paper/1405.7229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2014.

## Built on by (most cited first, 1 of 1)

- [Otsu based Differential Evolution Method for Image Segmentation](https://mlsanity.com/paper/2210.10005/) (2022)

## Abstract (arXiv)

This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is a heuristic algorithm motivated by the intelligent behavior of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. For the approximation scheme, each Gaussian function represents a pixel class and therefore a threshold. Unlike the Expectation Maximization (EM) algorithm, the ABC based method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time consuming computations commonly required by gradient-based methods. Experimental results demonstrate the algorithms ability to perform automatic multi threshold selection yet showing interesting advantages by comparison to other well known algorithms.